Wireless communication systems are known in which mobile units (e.g., in-car mobile or in-hand portable radios) wirelessly communicate with a fixed communication infrastructure comprising a plurality of geographically-diverse transceivers. In such systems, methods for determining location information for a given mobile unit are known. In particular, the well-known weighted least squares (WLS) solution can be used to determine location information as shown, for example, in U.S. Pat. No. 5,416,712 issued to Geier et al.
Succinctly stated, the WLS approach to location determination attempts to iteratively derive a location estimate for a mobile unit based, in part, on distance estimates between the mobile unit and fixed transmitters having known locations. Given that distance can be calculated as the product of velocity and time, the distance estimates (referred to as pseudo-ranges or PRs) are calculated in practice by multiplying the propagation delays between the mobile unit and fixed transmitters with the speed of light. Assuming ideally measured propagation delays, the location of the mobile unit can be calculated using the pseudo-ranges with little or no error. However, propagation delays are measured in practice using transmitted signals, which signals are subject to the effects of various error sources, e.g., noise, multipath interference, distortion, etc. The resultant errors in the delay measurements are translated into errors in the pseudo-ranges and, consequently, into error in the location estimate.
In order to combat the presence of measurement errors, the WLS solution factors the reliability of the various measurements into the location estimation. That is, the WLS solution, when used to estimate location, places greater reliance on measurements having greater reliability, and discounts those measurements having less reliability. In this manner, the WLS approach offers a significant performance advantage over non-weighted techniques. In practice, however, the reliability of a signal cannot be directly measured, but must be described as the variance of a stochastic variable.
Various methods for incorporating measurement reliability into a location determination context are known in the art. For example, in U.S. Pat. No. 5,202,829 issued to Geier, Kalman filters are used to assess the "quality" of pseudo-ranges measured using GPS receivers located in a ship and accompanying tailbuoys. Additionally, in U.S. Pat. No. 5,436,632 issued to Sheynblat, a system is disclosed wherein redundant GPS receivers at known locations (reference stations and integrity monitors) are employed to provide corrections for pseudo-range measurements made by a mobile unit also equipped with a GPS receiver. Sheynblat discusses the use of a WLS solution in which "measurement error covariance" is determined based on receiver noise as represented by errors between a given reference station and its corresponding integrity monitor.
While Geier and Sheynblat incorporate reliability determinations to improve the accuracy of location estimates, such techniques are not readily adaptable to mobile and portable wireless communication environments. First, both Geier and Sheynblat require the use of GPS receivers. Such receivers add significant cost to mobile/portable equipment and prohibitively add to the size and complexity of such equipment, particularly portable radios.
Additionally, as noted in '712 Geier, the use of Kalman filters requires significantly more computing power than a WLS solution. In portable units, computing power is often limited by size and battery life considerations, making the use of the computationally-expensive Kalman filter approach less attractive. Therefore, a need exists for a method which incorporates the advantages of a WLS location solution without the need for costly GPS equipment. In particular, what is needed is a technique for estimating a covariance matrix for use in a WLS solution.